Deep Learning With Python by Francois Chollet

Author:Francois Chollet [Chollet, Francois]
Language: eng
Format: epub
ISBN: 9781617294433
Google: Yo3CAQAACAAJ
Amazon: 1617294438
Publisher: Manning Publications
Published: 2017-10-27T21:00:00+00:00

Figure 6.2. Whereas word representations obtained from one-hot encoding or hashing are sparse, high-dimensional, and hardcoded, word embeddings are dense, relatively lowdimensional, and learned from data.

There are two ways to obtain word embeddings:

Learn word embeddings jointly with the main task you care about (such as document classification or sentiment prediction). In this setup, you start with random word vectors and then learn word vectors in the same way you learn the weights of a neural network.

Load into your model word embeddings that were precomputed using a different machine-learning task than the one you’re trying to solve. These are called pretrained word embeddings.

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